S134: INTRA-PATIENT FUNCTIONAL HETEROGENEITY OF AML DETERMINES FIRST-LINE TREATMENT RESPONSE
Authors: Y. Severin, Y. Festl, M. Roiss, T. Benoit, T. Heinemann, R. Wegmann, A.-K. Kienzler, M. Bissig, M. Scharl, M. Manz, A. M. Müller, B. Snijder
Journal: HemaSphere (2022)

Background: Acute myeloid leukemia (AML) is a heterogeneous disease with limited treatment options and poor long-term survival. AML is characterized by a fast clonal expansion of premature myeloid cells with highly variable combinations of chromosomal aberrations and somatic mutations. Patient-to-patient variability is further complicated by the fact that AML partially recapitulates myeloid maturation with a rare leukemic stem cell population at the top of the hierarchy. Although eradication of bulk tumor cells is necessary, matched targeted therapies against the AML stem cell compartment are likely essential for a long lasting cure of patients. Despite this large tumor heterogeneity in de-novo AML patients, most patients receive the same standard-of-care treatment resulting in large differences of treatment success and overall survival. Functional ex vivo drug-response profiling offers a possible route to tailor individual treatments for AML patients. However, the intra-patient functional heterogeneity of divergent AML cell subpopulations, and their contribution to clinical outcome, have not yet been systematically studied. Aims: This study has two aims: 1) To analyze the intra-patient functional heterogeneity of AML blasts, and their contribution to the response to first-line treatment. 2) To improve the clinical response predictions from functional ex vivo drug response profiling by explicit analysis of functional tumor heterogeneity. Methods: In this prospective, non-interventional study, 180 AML patient biopsies were collected with written informed consent (from 44 patients with newly diagnosed AML undergoing intensive induction chemotherapy). Patient matched bone marrow and blood samples were obtained for each donor at three different time points across the course of the treatment. For each sample, we perform ex vivo drug-response profiling analyzed by multiplexed immunofluorescence, automated microscopy, and deep learning-based morphological profiling to determine drug sensitivities on a single-cell level. The drug library includes 100 distinct drugs and drug combinations, including standard of care induction chemotherapy, and single-cell drug sensitivity data is analyzed as a function of cellular clone and blast maturation state. The drug response data is integrated with patient-matched serum cytokine profiling, RNA-sequencing, and clinical data, allowing to identify the cellular subpopulations whose functional response is predictive of treatment response. Results: Our workflow excelled at predicting patient responses to first line treatment, with an overall predictive power of 85% accuracy and a diagnostic odds ratio of 47. Molecular and morphology-based phenotyping of cancer subpopulations strengthened clinical associations and revealed blast maturity as well as immune activation to be strong predictors of treatment success and the risk of chemotherapy-related infectious complications. Furthermore, the screening of 100 unique drugs and combinations enabled individualized ex vivo treatment predictions, suggesting more potent treatment options than those currently deployed. Image:Summary/Conclusion: Maturation-associated intra-tumor drug response heterogeneity is a strong predictor of clinical response to first-line intensive induction chemotherapy in AML. Our multiplexed ex vivo drug screening framework is highly flexible, allowing us to discover, molecularly describe, and target clinically relevant AML subpopulations on a personal basis, leading to improved personalized treatment recommendations.